In the high-dimensional case, we see fewer differences between the datasets. The same complementarity between GradCAM/KernelSHAP and DeepSHAP /DeepLIFT /ExpectedGradients is again noticeable for all three datasets, suggesting that it is linked to the complexity or dimensionality of the classification problem. We also note that GradCAM and KernelSHAP significantly outperform the other methods on Impact Coverage (COV), which could only be computed for high-dimensional datasets. This complementarity can be explained by examining the inner workings of the methods. GradCAM works by upsampling features in the last convolutional layer [1]}, and KernelSHAP uses an image segmentation step before computing the actual Shapley values [2]}. These properties result in very non-granular attribution maps. Conversely, DeepLIFT, DeepSHAP and ExpectedGradients are all based on modified versions of the gradient, which tends to produce very granular attribution maps. This difference in granularity could be the source of the observed complementarity.
